Overview

Dataset statistics

Number of variables28
Number of observations4842
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory224.0 B

Variable types

Text3
Numeric10
Categorical15

Alerts

Score is highly overall correlated with Real Guest Cleanlines Score and 5 other fieldsHigh correlation
Reviews is highly overall correlated with Booked todayHigh correlation
Booked today is highly overall correlated with ReviewsHigh correlation
Real Guest Cleanlines Score is highly overall correlated with Score and 5 other fieldsHigh correlation
Real Guest Facilities Score is highly overall correlated with Score and 5 other fieldsHigh correlation
Real Guest Location Score is highly overall correlated with Score and 4 other fieldsHigh correlation
Real Guest Service Score is highly overall correlated with Score and 5 other fieldsHigh correlation
Real Guest Value for money Score is highly overall correlated with Score and 5 other fieldsHigh correlation
Sparkling clean is highly overall correlated with Score and 4 other fieldsHigh correlation
NewlyBuilt is highly imbalanced (85.4%)Imbalance
ExcellentView is highly imbalanced (72.2%)Imbalance
Free WiFi In All Rooms is highly imbalanced (70.1%)Imbalance
Kids club is highly imbalanced (66.4%)Imbalance
Stars has 552 (11.4%) zerosZeros
Booked today has 3451 (71.3%) zerosZeros

Reproduction

Analysis started2023-06-15 20:58:32.524824
Analysis finished2023-06-15 20:59:05.255559
Duration32.73 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

Name
Text

Distinct4302
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
2023-06-15T23:59:05.778266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length100
Median length65
Mean length25.611937
Min length3

Characters and Unicode

Total characters124013
Distinct characters257
Distinct categories14 ?
Distinct scripts7 ?
Distinct blocks9 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3802 ?
Unique (%)78.5%

Sample

1st rowAsia Hotel Bangkok (SHA Plus+)
2nd rowRembrandt Hotel & Suites (SHA Plus+)
3rd rowDream Hotel Bangkok (SHA Plus+)
4th rowVIX Bangkok @ Victory Monument
5th rowThe Berkeley Hotel Pratunam (SHA Plus+)
ValueCountFrequency (%)
hotel 1296
 
6.4%
sha 1135
 
5.6%
plus 1045
 
5.2%
resort 633
 
3.1%
bangkok 527
 
2.6%
phuket 493
 
2.4%
the 474
 
2.3%
extra 471
 
2.3%
337
 
1.7%
hostel 305
 
1.5%
Other values (3809) 13528
66.8%
2023-06-15T23:59:06.651083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15402
 
12.4%
e 9723
 
7.8%
a 9356
 
7.5%
o 7196
 
5.8%
t 6956
 
5.6%
n 5691
 
4.6%
l 5343
 
4.3%
i 4653
 
3.8%
s 4598
 
3.7%
u 4386
 
3.5%
Other values (247) 50709
40.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 79581
64.2%
Uppercase Letter 23541
 
19.0%
Space Separator 15402
 
12.4%
Decimal Number 1335
 
1.1%
Close Punctuation 1151
 
0.9%
Open Punctuation 1149
 
0.9%
Math Symbol 561
 
0.5%
Other Punctuation 513
 
0.4%
Other Letter 468
 
0.4%
Dash Punctuation 211
 
0.2%
Other values (4) 101
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
า 30
 
6.4%
น 29
 
6.2%
อ 24
 
5.1%
ร 23
 
4.9%
เ 22
 
4.7%
ล 15
 
3.2%
ง 15
 
3.2%
ม 14
 
3.0%
บ 13
 
2.8%
ส 13
 
2.8%
Other values (111) 270
57.7%
Lowercase Letter
ValueCountFrequency (%)
e 9723
12.2%
a 9356
11.8%
o 7196
9.0%
t 6956
8.7%
n 5691
 
7.2%
l 5343
 
6.7%
i 4653
 
5.8%
s 4598
 
5.8%
u 4386
 
5.5%
r 4330
 
5.4%
Other values (46) 17349
21.8%
Uppercase Letter
ValueCountFrequency (%)
H 3500
14.9%
P 2967
12.6%
S 2813
11.9%
A 2099
8.9%
B 1962
 
8.3%
R 1579
 
6.7%
T 1165
 
4.9%
E 889
 
3.8%
C 843
 
3.6%
M 669
 
2.8%
Other values (20) 5055
21.5%
Other Punctuation
ValueCountFrequency (%)
& 191
37.2%
@ 113
22.0%
. 70
 
13.6%
' 58
 
11.3%
, 46
 
9.0%
/ 10
 
1.9%
! 8
 
1.6%
• 6
 
1.2%
: 6
 
1.2%
# 3
 
0.6%
Other values (2) 2
 
0.4%
Nonspacing Mark
ValueCountFrequency (%)
ิ 18
20.7%
้ 17
19.5%
์ 14
16.1%
่ 11
12.6%
ี 9
10.3%
ั 5
 
5.7%
ุ 4
 
4.6%
ื 4
 
4.6%
ู 2
 
2.3%
็ 1
 
1.1%
Other values (2) 2
 
2.3%
Decimal Number
ValueCountFrequency (%)
1 263
19.7%
2 236
17.7%
3 143
10.7%
4 141
10.6%
8 111
8.3%
5 110
8.2%
9 93
 
7.0%
7 85
 
6.4%
0 84
 
6.3%
6 69
 
5.2%
Close Punctuation
ValueCountFrequency (%)
) 1137
98.8%
] 9
 
0.8%
】 3
 
0.3%
） 2
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 1135
98.8%
[ 9
 
0.8%
【 3
 
0.3%
（ 2
 
0.2%
Math Symbol
ValueCountFrequency (%)
+ 560
99.8%
~ 1
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 207
98.1%
– 4
 
1.9%
Space Separator
ValueCountFrequency (%)
15402
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 5
100.0%
Other Symbol
ValueCountFrequency (%)
❤ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 102978
83.0%
Common 20336
 
16.4%
Thai 421
 
0.3%
Cyrillic 144
 
0.1%
Han 114
 
0.1%
Arabic 19
 
< 0.1%
Inherited 1
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
店 6
 
5.3%
酒 6
 
5.3%
寓 4
 
3.5%
东 4
 
3.5%
公 4
 
3.5%
海 3
 
2.6%
吉 3
 
2.6%
普 3
 
2.6%
景 2
 
1.8%
栈 2
 
1.8%
Other values (64) 77
67.5%
Latin
ValueCountFrequency (%)
e 9723
 
9.4%
a 9356
 
9.1%
o 7196
 
7.0%
t 6956
 
6.8%
n 5691
 
5.5%
l 5343
 
5.2%
i 4653
 
4.5%
s 4598
 
4.5%
u 4386
 
4.3%
r 4330
 
4.2%
Other values (47) 40746
39.6%
Thai
ValueCountFrequency (%)
า 30
 
7.1%
น 29
 
6.9%
อ 24
 
5.7%
ร 23
 
5.5%
เ 22
 
5.2%
ิ 18
 
4.3%
้ 17
 
4.0%
ล 15
 
3.6%
ง 15
 
3.6%
์ 14
 
3.3%
Other values (35) 214
50.8%
Common
ValueCountFrequency (%)
15402
75.7%
) 1137
 
5.6%
( 1135
 
5.6%
+ 560
 
2.8%
1 263
 
1.3%
2 236
 
1.2%
- 207
 
1.0%
& 191
 
0.9%
3 143
 
0.7%
4 141
 
0.7%
Other values (28) 921
 
4.5%
Cyrillic
ValueCountFrequency (%)
а 20
13.9%
н 13
 
9.0%
е 12
 
8.3%
т 9
 
6.2%
о 9
 
6.2%
л 8
 
5.6%
в 8
 
5.6%
п 7
 
4.9%
м 6
 
4.2%
ы 6
 
4.2%
Other values (19) 46
31.9%
Arabic
ValueCountFrequency (%)
ا 4
21.1%
ل 2
10.5%
ر 2
10.5%
ع 2
10.5%
ب 1
 
5.3%
ش 1
 
5.3%
ة 1
 
5.3%
س 1
 
5.3%
م 1
 
5.3%
ق 1
 
5.3%
Other values (3) 3
15.8%
Inherited
ValueCountFrequency (%)
️ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123279
99.4%
Thai 421
 
0.3%
Cyrillic 144
 
0.1%
CJK 114
 
0.1%
Arabic 19
 
< 0.1%
None 19
 
< 0.1%
Punctuation 15
 
< 0.1%
VS 1
 
< 0.1%
Dingbats 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15402
 
12.5%
e 9723
 
7.9%
a 9356
 
7.6%
o 7196
 
5.8%
t 6956
 
5.6%
n 5691
 
4.6%
l 5343
 
4.3%
i 4653
 
3.8%
s 4598
 
3.7%
u 4386
 
3.6%
Other values (71) 49975
40.5%
Thai
ValueCountFrequency (%)
า 30
 
7.1%
น 29
 
6.9%
อ 24
 
5.7%
ร 23
 
5.5%
เ 22
 
5.2%
ิ 18
 
4.3%
้ 17
 
4.0%
ล 15
 
3.6%
ง 15
 
3.6%
์ 14
 
3.3%
Other values (35) 214
50.8%
Cyrillic
ValueCountFrequency (%)
а 20
13.9%
н 13
 
9.0%
е 12
 
8.3%
т 9
 
6.2%
о 9
 
6.2%
л 8
 
5.6%
в 8
 
5.6%
п 7
 
4.9%
м 6
 
4.2%
ы 6
 
4.2%
Other values (19) 46
31.9%
Punctuation
ValueCountFrequency (%)
• 6
40.0%
’ 5
33.3%
– 4
26.7%
CJK
ValueCountFrequency (%)
店 6
 
5.3%
酒 6
 
5.3%
寓 4
 
3.5%
东 4
 
3.5%
公 4
 
3.5%
海 3
 
2.6%
吉 3
 
2.6%
普 3
 
2.6%
景 2
 
1.8%
栈 2
 
1.8%
Other values (64) 77
67.5%
Arabic
ValueCountFrequency (%)
ا 4
21.1%
ل 2
10.5%
ر 2
10.5%
ع 2
10.5%
ب 1
 
5.3%
ش 1
 
5.3%
ة 1
 
5.3%
س 1
 
5.3%
م 1
 
5.3%
ق 1
 
5.3%
Other values (3) 3
15.8%
None
ValueCountFrequency (%)
ö 4
21.1%
】 3
15.8%
【 3
15.8%
（ 2
10.5%
） 2
10.5%
â 1
 
5.3%
é 1
 
5.3%
1
 
5.3%
à 1
 
5.3%
è 1
 
5.3%
VS
ValueCountFrequency (%)
️ 1
100.0%
Dingbats
ValueCountFrequency (%)
❤ 1
100.0%

Price
Real number (ℝ)

Distinct529
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148.91512
Minimum12
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-06-15T23:59:06.888780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile46
Q169
median97
Q3158
95-th percentile478
Maximum999
Range987
Interquartile range (IQR)89

Descriptive statistics

Standard deviation146.87833
Coefficient of variation (CV)0.98632248
Kurtosis9.0825155
Mean148.91512
Median Absolute Deviation (MAD)35
Skewness2.840883
Sum721047
Variance21573.243
MonotonicityNot monotonic
2023-06-15T23:59:07.063902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79 114
 
2.4%
69 99
 
2.0%
89 86
 
1.8%
99 84
 
1.7%
59 69
 
1.4%
49 61
 
1.3%
73 60
 
1.2%
90 59
 
1.2%
55 58
 
1.2%
72 57
 
1.2%
Other values (519) 4095
84.6%
ValueCountFrequency (%)
12 1
 
< 0.1%
19 1
 
< 0.1%
21 2
 
< 0.1%
22 3
 
0.1%
24 2
 
< 0.1%
25 1
 
< 0.1%
27 3
 
0.1%
28 2
 
< 0.1%
29 2
 
< 0.1%
30 8
0.2%
ValueCountFrequency (%)
999 1
< 0.1%
995 1
< 0.1%
987 1
< 0.1%
975 1
< 0.1%
968 2
< 0.1%
967 1
< 0.1%
964 1
< 0.1%
959 2
< 0.1%
957 1
< 0.1%
954 2
< 0.1%

Stars
Real number (ℝ)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8712309
Minimum0
Maximum5
Zeros552
Zeros (%)11.4%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-06-15T23:59:07.231018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3560869
Coefficient of variation (CV)0.4723016
Kurtosis0.043725352
Mean2.8712309
Median Absolute Deviation (MAD)1
Skewness-0.66585311
Sum13902.5
Variance1.8389718
MonotonicityNot monotonic
2023-06-15T23:59:07.383833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3 1546
31.9%
4 827
17.1%
2 680
14.0%
0 552
 
11.4%
5 411
 
8.5%
2.5 281
 
5.8%
3.5 261
 
5.4%
4.5 175
 
3.6%
1.5 58
 
1.2%
1 51
 
1.1%
ValueCountFrequency (%)
0 552
 
11.4%
1 51
 
1.1%
1.5 58
 
1.2%
2 680
14.0%
2.5 281
 
5.8%
3 1546
31.9%
3.5 261
 
5.4%
4 827
17.1%
4.5 175
 
3.6%
5 411
 
8.5%
ValueCountFrequency (%)
5 411
 
8.5%
4.5 175
 
3.6%
4 827
17.1%
3.5 261
 
5.4%
3 1546
31.9%
2.5 281
 
5.8%
2 680
14.0%
1.5 58
 
1.2%
1 51
 
1.1%
0 552
 
11.4%

Score
Real number (ℝ)

Distinct69
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1351301
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-06-15T23:59:07.574381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6.5
Q17.7
median8.3
Q38.7
95-th percentile9.4
Maximum10
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.94177249
Coefficient of variation (CV)0.11576612
Kurtosis5.4948763
Mean8.1351301
Median Absolute Deviation (MAD)0.5
Skewness-1.4889825
Sum39390.3
Variance0.88693542
MonotonicityNot monotonic
2023-06-15T23:59:07.815849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.4 308
 
6.4%
8.5 254
 
5.2%
8.3 243
 
5.0%
8.1 233
 
4.8%
8.7 232
 
4.8%
8.6 231
 
4.8%
8.8 230
 
4.8%
8.2 225
 
4.6%
7.9 223
 
4.6%
8 211
 
4.4%
Other values (59) 2452
50.6%
ValueCountFrequency (%)
2 3
0.1%
2.3 5
0.1%
2.5 1
 
< 0.1%
2.8 2
 
< 0.1%
3 2
 
< 0.1%
3.1 4
0.1%
3.3 1
 
< 0.1%
3.5 1
 
< 0.1%
3.6 4
0.1%
3.9 1
 
< 0.1%
ValueCountFrequency (%)
10 52
1.1%
9.9 12
 
0.2%
9.8 31
 
0.6%
9.7 16
 
0.3%
9.6 54
1.1%
9.5 52
1.1%
9.4 56
1.2%
9.3 82
1.7%
9.2 126
2.6%
9.1 127
2.6%

SpokenLanguages
Text

Distinct473
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
2023-06-15T23:59:08.227572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length389
Median length13
Mean length21.630318
Min length4

Characters and Unicode

Total characters104734
Distinct characters46
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique293 ?
Unique (%)6.1%

Sample

1st rowEnglish, Thai
2nd rowEnglish, Thai
3rd rowEnglish, Thai
4th rowEnglish, Chinese [Mandarin], Thai
5th rowEnglish, Chinese [Mandarin], Thai
ValueCountFrequency (%)
english 4708
33.2%
thai 4670
32.9%
chinese 1036
 
7.3%
mandarin 901
 
6.4%
french 355
 
2.5%
german 248
 
1.7%
russian 228
 
1.6%
burmese 225
 
1.6%
japanese 221
 
1.6%
filipino 172
 
1.2%
Other values (35) 1414
 
10.0%
2023-06-15T23:59:08.952664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 13089
12.5%
h 11133
10.6%
n 9852
9.4%
9336
8.9%
a 8666
 
8.3%
, 8300
 
7.9%
s 7132
 
6.8%
l 5161
 
4.9%
g 4758
 
4.5%
E 4714
 
4.5%
Other values (36) 22593
21.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 70848
67.6%
Uppercase Letter 14178
 
13.5%
Space Separator 9336
 
8.9%
Other Punctuation 8300
 
7.9%
Open Punctuation 1036
 
1.0%
Close Punctuation 1036
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 13089
18.5%
h 11133
15.7%
n 9852
13.9%
a 8666
12.2%
s 7132
10.1%
l 5161
 
7.3%
g 4758
 
6.7%
e 4265
 
6.0%
r 2039
 
2.9%
d 1153
 
1.6%
Other values (12) 3600
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
E 4714
33.2%
T 4711
33.2%
C 1190
 
8.4%
M 974
 
6.9%
F 534
 
3.8%
G 256
 
1.8%
B 232
 
1.6%
R 230
 
1.6%
J 221
 
1.6%
H 189
 
1.3%
Other values (10) 927
 
6.5%
Space Separator
ValueCountFrequency (%)
9336
100.0%
Other Punctuation
ValueCountFrequency (%)
, 8300
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 1036
100.0%
Close Punctuation
ValueCountFrequency (%)
] 1036
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 85026
81.2%
Common 19708
 
18.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 13089
15.4%
h 11133
13.1%
n 9852
11.6%
a 8666
10.2%
s 7132
8.4%
l 5161
 
6.1%
g 4758
 
5.6%
E 4714
 
5.5%
T 4711
 
5.5%
e 4265
 
5.0%
Other values (32) 11545
13.6%
Common
ValueCountFrequency (%)
9336
47.4%
, 8300
42.1%
[ 1036
 
5.3%
] 1036
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 104734
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 13089
12.5%
h 11133
10.6%
n 9852
9.4%
9336
8.9%
a 8666
 
8.3%
, 8300
 
7.9%
s 7132
 
6.8%
l 5161
 
4.9%
g 4758
 
4.5%
E 4714
 
4.5%
Other values (36) 22593
21.6%

Reviews
Real number (ℝ)

Distinct1744
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean988.16625
Minimum1
Maximum61617
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-06-15T23:59:09.202843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q160
median271
Q31004.5
95-th percentile4112
Maximum61617
Range61616
Interquartile range (IQR)944.5

Descriptive statistics

Standard deviation2270.4062
Coefficient of variation (CV)2.2975953
Kurtosis155.85816
Mean988.16625
Median Absolute Deviation (MAD)254
Skewness9.1260001
Sum4784701
Variance5154744.2
MonotonicityNot monotonic
2023-06-15T23:59:09.435373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 99
 
2.0%
2 85
 
1.8%
3 55
 
1.1%
5 47
 
1.0%
4 41
 
0.8%
8 36
 
0.7%
9 35
 
0.7%
6 33
 
0.7%
22 30
 
0.6%
10 29
 
0.6%
Other values (1734) 4352
89.9%
ValueCountFrequency (%)
1 99
2.0%
2 85
1.8%
3 55
1.1%
4 41
0.8%
5 47
1.0%
6 33
 
0.7%
7 24
 
0.5%
8 36
 
0.7%
9 35
 
0.7%
10 29
 
0.6%
ValueCountFrequency (%)
61617 1
< 0.1%
40320 1
< 0.1%
28839 1
< 0.1%
28073 1
< 0.1%
27771 2
< 0.1%
25454 1
< 0.1%
24128 1
< 0.1%
23462 1
< 0.1%
22016 1
< 0.1%
20544 1
< 0.1%

Location
Text

Distinct3815
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
2023-06-15T23:59:10.418018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length236
Median length136
Mean length71.880421
Min length22

Characters and Unicode

Total characters348045
Distinct characters153
Distinct categories14 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3308 ?
Unique (%)68.3%

Sample

1st row296 Phayathai Road, Siam, Bangkok, Thailand, 10400
2nd row19 Sukhumvit Soi 18, Klong Toei, Sukhumvit, Bangkok, Thailand, 10110
3rd row10 Sukhumvit Soi 15, Sukhumvit, Bangkok, Thailand, 10110
4th row13-15 Thanon Ratchawithi, Chatuchak, Bangkok, Thailand, 10400
5th row559 Ratchathewi, Pratunam, Bangkok, Thailand, 10400
ValueCountFrequency (%)
thailand 5050
 
10.3%
bangkok 2995
 
6.1%
phuket 2923
 
6.0%
road 1651
 
3.4%
soi 1467
 
3.0%
sukhumvit 1019
 
2.1%
patong 818
 
1.7%
rd 687
 
1.4%
83150 687
 
1.4%
moo 608
 
1.2%
Other values (5555) 31121
63.5%
2023-06-15T23:59:11.484021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
44184
 
12.7%
a 37187
 
10.7%
, 24189
 
6.9%
n 20323
 
5.8%
h 16973
 
4.9%
o 16382
 
4.7%
i 14366
 
4.1%
k 12359
 
3.6%
t 11812
 
3.4%
0 10291
 
3.0%
Other values (143) 139979
40.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 192240
55.2%
Space Separator 44184
 
12.7%
Decimal Number 41326
 
11.9%
Uppercase Letter 37934
 
10.9%
Other Punctuation 27782
 
8.0%
Other Letter 2485
 
0.7%
Dash Punctuation 1241
 
0.4%
Nonspacing Mark 403
 
0.1%
Open Punctuation 218
 
0.1%
Close Punctuation 217
 
0.1%
Other values (4) 15
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
น 221
 
8.9%
า 213
 
8.6%
อ 174
 
7.0%
ร 160
 
6.4%
ง 144
 
5.8%
เ 115
 
4.6%
ก 109
 
4.4%
ม 104
 
4.2%
ย 94
 
3.8%
ต 91
 
3.7%
Other values (47) 1060
42.7%
Uppercase Letter
ValueCountFrequency (%)
T 7439
19.6%
P 6316
16.6%
S 4534
12.0%
B 4445
11.7%
R 3812
10.0%
K 2957
 
7.8%
M 1778
 
4.7%
N 1157
 
3.1%
A 1147
 
3.0%
C 947
 
2.5%
Other values (17) 3402
9.0%
Lowercase Letter
ValueCountFrequency (%)
a 37187
19.3%
n 20323
10.6%
h 16973
8.8%
o 16382
8.5%
i 14366
 
7.5%
k 12359
 
6.4%
t 11812
 
6.1%
u 9112
 
4.7%
d 8901
 
4.6%
g 8425
 
4.4%
Other values (16) 36400
18.9%
Decimal Number
ValueCountFrequency (%)
0 10291
24.9%
1 9831
23.8%
3 4457
10.8%
8 3775
 
9.1%
2 3735
 
9.0%
5 2597
 
6.3%
4 2271
 
5.5%
6 1586
 
3.8%
9 1471
 
3.6%
7 1311
 
3.2%
Other Punctuation
ValueCountFrequency (%)
, 24189
87.1%
/ 2380
 
8.6%
. 1187
 
4.3%
& 12
 
< 0.1%
; 4
 
< 0.1%
: 3
 
< 0.1%
# 3
 
< 0.1%
@ 2
 
< 0.1%
* 1
 
< 0.1%
، 1
 
< 0.1%
Nonspacing Mark
ValueCountFrequency (%)
ิ 59
14.6%
ั 54
13.4%
ุ 49
12.2%
่ 46
11.4%
้ 45
11.2%
ู 42
10.4%
ี 40
9.9%
์ 32
7.9%
ื 23
 
5.7%
็ 13
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 1239
99.8%
– 2
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 216
99.1%
[ 2
 
0.9%
Close Punctuation
ValueCountFrequency (%)
) 215
99.1%
] 2
 
0.9%
Math Symbol
ValueCountFrequency (%)
+ 6
60.0%
| 4
40.0%
Space Separator
ValueCountFrequency (%)
44184
100.0%
Initial Punctuation
ValueCountFrequency (%)
“ 2
100.0%
Currency Symbol
ValueCountFrequency (%)
€ 2
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 230174
66.1%
Common 114982
33.0%
Thai 2881
 
0.8%
Arabic 8
 
< 0.1%

Most frequent character per script

Thai
ValueCountFrequency (%)
น 221
 
7.7%
า 213
 
7.4%
อ 174
 
6.0%
ร 160
 
5.6%
ง 144
 
5.0%
เ 115
 
4.0%
ก 109
 
3.8%
ม 104
 
3.6%
ย 94
 
3.3%
ต 91
 
3.2%
Other values (50) 1456
50.5%
Latin
ValueCountFrequency (%)
a 37187
16.2%
n 20323
 
8.8%
h 16973
 
7.4%
o 16382
 
7.1%
i 14366
 
6.2%
k 12359
 
5.4%
t 11812
 
5.1%
u 9112
 
4.0%
d 8901
 
3.9%
g 8425
 
3.7%
Other values (43) 74334
32.3%
Common
ValueCountFrequency (%)
44184
38.4%
, 24189
21.0%
0 10291
 
9.0%
1 9831
 
8.6%
3 4457
 
3.9%
8 3775
 
3.3%
2 3735
 
3.2%
5 2597
 
2.3%
/ 2380
 
2.1%
4 2271
 
2.0%
Other values (22) 7272
 
6.3%
Arabic
ValueCountFrequency (%)
س 1
12.5%
ة 1
12.5%
م 1
12.5%
ا 1
12.5%
ق 1
12.5%
د 1
12.5%
ن 1
12.5%
ف 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 345146
99.2%
Thai 2881
 
0.8%
Arabic 9
 
< 0.1%
Punctuation 5
 
< 0.1%
None 2
 
< 0.1%
Currency Symbols 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
44184
 
12.8%
a 37187
 
10.8%
, 24189
 
7.0%
n 20323
 
5.9%
h 16973
 
4.9%
o 16382
 
4.7%
i 14366
 
4.2%
k 12359
 
3.6%
t 11812
 
3.4%
0 10291
 
3.0%
Other values (69) 137080
39.7%
Thai
ValueCountFrequency (%)
น 221
 
7.7%
า 213
 
7.4%
อ 174
 
6.0%
ร 160
 
5.6%
ง 144
 
5.0%
เ 115
 
4.0%
ก 109
 
3.8%
ม 104
 
3.6%
ย 94
 
3.3%
ต 91
 
3.2%
Other values (50) 1456
50.5%
Punctuation
ValueCountFrequency (%)
– 2
40.0%
“ 2
40.0%
’ 1
20.0%
None
ValueCountFrequency (%)
 2
100.0%
Currency Symbols
ValueCountFrequency (%)
€ 2
100.0%
Arabic
ValueCountFrequency (%)
س 1
11.1%
ة 1
11.1%
م 1
11.1%
ا 1
11.1%
ق 1
11.1%
د 1
11.1%
ن 1
11.1%
ف 1
11.1%
، 1
11.1%

Sparkling clean
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
0
3480 
1
1362 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4842
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 3480
71.9%
1 1362
 
28.1%

Length

2023-06-15T23:59:11.706529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T23:59:11.877201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3480
71.9%
1 1362
 
28.1%

Most occurring characters

ValueCountFrequency (%)
0 3480
71.9%
1 1362
 
28.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4842
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3480
71.9%
1 1362
 
28.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4842
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3480
71.9%
1 1362
 
28.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3480
71.9%
1 1362
 
28.1%

NewlyBuilt
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
0
4741 
1
 
101

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4842
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4741
97.9%
1 101
 
2.1%

Length

2023-06-15T23:59:11.984709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T23:59:12.108768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4741
97.9%
1 101
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 4741
97.9%
1 101
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4842
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4741
97.9%
1 101
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4842
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4741
97.9%
1 101
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4741
97.9%
1 101
 
2.1%

ExcellentView
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
0
4609 
1
 
233

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4842
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4609
95.2%
1 233
 
4.8%

Length

2023-06-15T23:59:12.220959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T23:59:12.351458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4609
95.2%
1 233
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 4609
95.2%
1 233
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4842
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4609
95.2%
1 233
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Common 4842
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4609
95.2%
1 233
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4609
95.2%
1 233
 
4.8%

Check In 24/7
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
0
2969 
1
1873 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4842
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 2969
61.3%
1 1873
38.7%

Length

2023-06-15T23:59:12.462038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T23:59:12.601953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2969
61.3%
1 1873
38.7%

Most occurring characters

ValueCountFrequency (%)
0 2969
61.3%
1 1873
38.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4842
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2969
61.3%
1 1873
38.7%

Most occurring scripts

ValueCountFrequency (%)
Common 4842
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2969
61.3%
1 1873
38.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2969
61.3%
1 1873
38.7%

AirportTransfer
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
0
2661 
1
2181 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4842
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 2661
55.0%
1 2181
45.0%

Length

2023-06-15T23:59:12.723438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T23:59:12.870988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2661
55.0%
1 2181
45.0%

Most occurring characters

ValueCountFrequency (%)
0 2661
55.0%
1 2181
45.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4842
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2661
55.0%
1 2181
45.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4842
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2661
55.0%
1 2181
45.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2661
55.0%
1 2181
45.0%

Front Desk
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
1
3013 
0
1829 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4842
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 3013
62.2%
0 1829
37.8%

Length

2023-06-15T23:59:12.989958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T23:59:13.112188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3013
62.2%
0 1829
37.8%

Most occurring characters

ValueCountFrequency (%)
1 3013
62.2%
0 1829
37.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4842
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3013
62.2%
0 1829
37.8%

Most occurring scripts

ValueCountFrequency (%)
Common 4842
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3013
62.2%
0 1829
37.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3013
62.2%
0 1829
37.8%

Valet Parking
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
0
4117 
1
725 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4842
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4117
85.0%
1 725
 
15.0%

Length

2023-06-15T23:59:13.223705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T23:59:13.358219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4117
85.0%
1 725
 
15.0%

Most occurring characters

ValueCountFrequency (%)
0 4117
85.0%
1 725
 
15.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4842
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4117
85.0%
1 725
 
15.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4842
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4117
85.0%
1 725
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4117
85.0%
1 725
 
15.0%

Free WiFi In All Rooms
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
1
4585 
0
 
257

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4842
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 4585
94.7%
0 257
 
5.3%

Length

2023-06-15T23:59:13.479348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T23:59:13.617866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 4585
94.7%
0 257
 
5.3%

Most occurring characters

ValueCountFrequency (%)
1 4585
94.7%
0 257
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4842
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4585
94.7%
0 257
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common 4842
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4585
94.7%
0 257
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4585
94.7%
0 257
 
5.3%

Swimming Pool
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
0
3704 
1
1138 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4842
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3704
76.5%
1 1138
 
23.5%

Length

2023-06-15T23:59:13.984712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T23:59:14.117230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3704
76.5%
1 1138
 
23.5%

Most occurring characters

ValueCountFrequency (%)
0 3704
76.5%
1 1138
 
23.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4842
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3704
76.5%
1 1138
 
23.5%

Most occurring scripts

ValueCountFrequency (%)
Common 4842
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3704
76.5%
1 1138
 
23.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3704
76.5%
1 1138
 
23.5%

Bar
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
0
2697 
1
2145 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4842
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 2697
55.7%
1 2145
44.3%

Length

2023-06-15T23:59:14.229451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T23:59:14.360862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2697
55.7%
1 2145
44.3%

Most occurring characters

ValueCountFrequency (%)
0 2697
55.7%
1 2145
44.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4842
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2697
55.7%
1 2145
44.3%

Most occurring scripts

ValueCountFrequency (%)
Common 4842
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2697
55.7%
1 2145
44.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2697
55.7%
1 2145
44.3%

Coffee
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
1
2818 
0
2024 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4842
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2818
58.2%
0 2024
41.8%

Length

2023-06-15T23:59:14.479677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T23:59:14.615935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2818
58.2%
0 2024
41.8%

Most occurring characters

ValueCountFrequency (%)
1 2818
58.2%
0 2024
41.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4842
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2818
58.2%
0 2024
41.8%

Most occurring scripts

ValueCountFrequency (%)
Common 4842
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2818
58.2%
0 2024
41.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2818
58.2%
0 2024
41.8%

DailyHousekeeping
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
1
3979 
0
863 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4842
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 3979
82.2%
0 863
 
17.8%

Length

2023-06-15T23:59:14.723328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T23:59:14.849788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3979
82.2%
0 863
 
17.8%

Most occurring characters

ValueCountFrequency (%)
1 3979
82.2%
0 863
 
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4842
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3979
82.2%
0 863
 
17.8%

Most occurring scripts

ValueCountFrequency (%)
Common 4842
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3979
82.2%
0 863
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3979
82.2%
0 863
 
17.8%

Golf
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
0
4061 
1
781 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4842
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4061
83.9%
1 781
 
16.1%

Length

2023-06-15T23:59:14.962542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T23:59:15.089818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4061
83.9%
1 781
 
16.1%

Most occurring characters

ValueCountFrequency (%)
0 4061
83.9%
1 781
 
16.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4842
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4061
83.9%
1 781
 
16.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4842
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4061
83.9%
1 781
 
16.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4061
83.9%
1 781
 
16.1%

Kids club
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
0
4541 
1
 
301

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4842
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4541
93.8%
1 301
 
6.2%

Length

2023-06-15T23:59:15.202085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T23:59:15.332543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4541
93.8%
1 301
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 4541
93.8%
1 301
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4842
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4541
93.8%
1 301
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 4842
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4541
93.8%
1 301
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4541
93.8%
1 301
 
6.2%

Booked today
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6439488
Minimum0
Maximum176
Zeros3451
Zeros (%)71.3%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-06-15T23:59:15.480734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile20
Maximum176
Range176
Interquartile range (IQR)3

Descriptive statistics

Standard deviation9.4836117
Coefficient of variation (CV)2.6025645
Kurtosis71.621499
Mean3.6439488
Median Absolute Deviation (MAD)0
Skewness6.3261398
Sum17644
Variance89.93889
MonotonicityNot monotonic
2023-06-15T23:59:15.682382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3451
71.3%
3 189
 
3.9%
4 156
 
3.2%
5 119
 
2.5%
6 107
 
2.2%
7 78
 
1.6%
8 67
 
1.4%
9 63
 
1.3%
10 55
 
1.1%
11 48
 
1.0%
Other values (55) 509
 
10.5%
ValueCountFrequency (%)
0 3451
71.3%
3 189
 
3.9%
4 156
 
3.2%
5 119
 
2.5%
6 107
 
2.2%
7 78
 
1.6%
8 67
 
1.4%
9 63
 
1.3%
10 55
 
1.1%
11 48
 
1.0%
ValueCountFrequency (%)
176 2
< 0.1%
128 1
 
< 0.1%
124 1
 
< 0.1%
109 1
 
< 0.1%
99 1
 
< 0.1%
87 1
 
< 0.1%
82 1
 
< 0.1%
74 1
 
< 0.1%
70 3
0.1%
68 3
0.1%

Real Guest Cleanlines Score
Real number (ℝ)

Distinct68
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2682776
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-06-15T23:59:15.885066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6.2
Q17.7
median8.5
Q39
95-th percentile9.8
Maximum10
Range8
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.1735436
Coefficient of variation (CV)0.14193326
Kurtosis5.2493463
Mean8.2682776
Median Absolute Deviation (MAD)0.6
Skewness-1.6923469
Sum40035
Variance1.3772046
MonotonicityNot monotonic
2023-06-15T23:59:16.072788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.8 245
 
5.1%
9 226
 
4.7%
8.7 214
 
4.4%
8 203
 
4.2%
8.9 201
 
4.2%
8.6 199
 
4.1%
8.2 197
 
4.1%
10 195
 
4.0%
8.3 194
 
4.0%
9.1 193
 
4.0%
Other values (58) 2775
57.3%
ValueCountFrequency (%)
2 17
0.4%
2.2 2
 
< 0.1%
2.3 2
 
< 0.1%
2.5 14
0.3%
2.7 1
 
< 0.1%
3.3 2
 
< 0.1%
3.8 1
 
< 0.1%
3.9 2
 
< 0.1%
4 18
0.4%
4.2 2
 
< 0.1%
ValueCountFrequency (%)
10 195
4.0%
9.9 22
 
0.5%
9.8 39
 
0.8%
9.7 60
 
1.2%
9.6 87
1.8%
9.5 109
2.3%
9.4 128
2.6%
9.3 149
3.1%
9.2 183
3.8%
9.1 193
4.0%

Real Guest Facilities Score
Real number (ℝ)

Distinct71
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7749071
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-06-15T23:59:16.274805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5.8
Q17.2
median7.9
Q38.5
95-th percentile9.4
Maximum10
Range8
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.2021276
Coefficient of variation (CV)0.15461633
Kurtosis3.4920774
Mean7.7749071
Median Absolute Deviation (MAD)0.7
Skewness-1.2381396
Sum37646.1
Variance1.4451108
MonotonicityNot monotonic
2023-06-15T23:59:16.491217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 233
 
4.8%
8.2 224
 
4.6%
8.1 201
 
4.2%
8.3 199
 
4.1%
7.5 195
 
4.0%
7.8 189
 
3.9%
8.5 186
 
3.8%
8.4 180
 
3.7%
7.9 178
 
3.7%
7.3 166
 
3.4%
Other values (61) 2891
59.7%
ValueCountFrequency (%)
2 18
0.4%
2.5 18
0.4%
2.7 2
 
< 0.1%
3 5
 
0.1%
3.3 8
0.2%
3.4 2
 
< 0.1%
3.5 3
 
0.1%
3.6 1
 
< 0.1%
3.7 1
 
< 0.1%
3.8 1
 
< 0.1%
ValueCountFrequency (%)
10 142
2.9%
9.9 1
 
< 0.1%
9.8 7
 
0.1%
9.7 17
 
0.4%
9.6 23
 
0.5%
9.5 42
 
0.9%
9.4 44
 
0.9%
9.3 72
1.5%
9.2 67
1.4%
9.1 70
1.4%

Real Guest Location Score
Real number (ℝ)

Distinct67
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1292235
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-06-15T23:59:16.698513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6.4
Q17.6
median8.3
Q38.8
95-th percentile9.5
Maximum10
Range8
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation1.062253
Coefficient of variation (CV)0.13067091
Kurtosis6.0198249
Mean8.1292235
Median Absolute Deviation (MAD)0.6
Skewness-1.6340934
Sum39361.7
Variance1.1283815
MonotonicityNot monotonic
2023-06-15T23:59:16.885102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 270
 
5.6%
8.4 244
 
5.0%
8.8 229
 
4.7%
8.3 227
 
4.7%
8.5 221
 
4.6%
8.6 211
 
4.4%
8.7 205
 
4.2%
8.1 201
 
4.2%
8.2 199
 
4.1%
8.9 192
 
4.0%
Other values (57) 2643
54.6%
ValueCountFrequency (%)
2 12
0.2%
2.5 14
0.3%
2.7 1
 
< 0.1%
3 7
0.1%
3.2 1
 
< 0.1%
3.4 1
 
< 0.1%
3.5 3
 
0.1%
3.8 1
 
< 0.1%
3.9 1
 
< 0.1%
4 8
0.2%
ValueCountFrequency (%)
10 140
2.9%
9.9 6
 
0.1%
9.8 15
 
0.3%
9.7 20
 
0.4%
9.6 39
 
0.8%
9.5 63
1.3%
9.4 73
1.5%
9.3 116
2.4%
9.2 137
2.8%
9.1 141
2.9%

Real Guest Service Score
Real number (ℝ)

Distinct68
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1993804
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-06-15T23:59:17.218990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q17.6
median8.4
Q39
95-th percentile9.9
Maximum10
Range8
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.2165795
Coefficient of variation (CV)0.14837457
Kurtosis3.9714193
Mean8.1993804
Median Absolute Deviation (MAD)0.7
Skewness-1.4614942
Sum39701.4
Variance1.4800657
MonotonicityNot monotonic
2023-06-15T23:59:17.539957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 234
 
4.8%
8 230
 
4.8%
9 209
 
4.3%
8.7 208
 
4.3%
8.9 207
 
4.3%
8.8 197
 
4.1%
8.3 191
 
3.9%
8.6 189
 
3.9%
8.5 187
 
3.9%
8.4 171
 
3.5%
Other values (58) 2819
58.2%
ValueCountFrequency (%)
2 16
0.3%
2.5 14
0.3%
2.7 3
 
0.1%
3 1
 
< 0.1%
3.3 2
 
< 0.1%
3.5 3
 
0.1%
3.8 4
 
0.1%
4 26
0.5%
4.1 1
 
< 0.1%
4.2 1
 
< 0.1%
ValueCountFrequency (%)
10 234
4.8%
9.9 15
 
0.3%
9.8 51
 
1.1%
9.7 59
 
1.2%
9.6 80
 
1.7%
9.5 86
 
1.8%
9.4 112
2.3%
9.3 162
3.3%
9.2 171
3.5%
9.1 146
3.0%

Real Guest Value for money Score
Real number (ℝ)

Distinct68
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3039653
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.0 KiB
2023-06-15T23:59:17.790636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6.3
Q17.8
median8.5
Q39
95-th percentile9.9
Maximum10
Range8
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation1.1247294
Coefficient of variation (CV)0.13544486
Kurtosis6.2253662
Mean8.3039653
Median Absolute Deviation (MAD)0.6
Skewness-1.7841491
Sum40207.8
Variance1.2650163
MonotonicityNot monotonic
2023-06-15T23:59:17.982202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.8 293
 
6.1%
9 239
 
4.9%
8.9 228
 
4.7%
8.6 221
 
4.6%
10 217
 
4.5%
8.4 211
 
4.4%
8.5 206
 
4.3%
8 205
 
4.2%
8.3 198
 
4.1%
8.7 196
 
4.0%
Other values (58) 2628
54.3%
ValueCountFrequency (%)
2 17
0.4%
2.1 2
 
< 0.1%
2.5 11
0.2%
3 2
 
< 0.1%
3.2 1
 
< 0.1%
3.3 3
 
0.1%
3.5 1
 
< 0.1%
3.8 3
 
0.1%
3.9 1
 
< 0.1%
4 18
0.4%
ValueCountFrequency (%)
10 217
4.5%
9.9 36
 
0.7%
9.8 52
 
1.1%
9.7 47
 
1.0%
9.6 72
 
1.5%
9.5 78
 
1.6%
9.4 104
2.1%
9.3 116
2.4%
9.2 178
3.7%
9.1 174
3.6%

Origin
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.0 KiB
Bangkok
2480 
Phuket
2123 
Ko Pha-ngan
 
113
Ko Phi Phi
 
110
Koh Samui
 
16

Length

Max length11
Median length7
Mean length6.7296572
Min length6

Characters and Unicode

Total characters32585
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBangkok
2nd rowBangkok
3rd rowBangkok
4th rowBangkok
5th rowBangkok

Common Values

ValueCountFrequency (%)
Bangkok 2480
51.2%
Phuket 2123
43.8%
Ko Pha-ngan 113
 
2.3%
Ko Phi Phi 110
 
2.3%
Koh Samui 16
 
0.3%

Length

2023-06-15T23:59:18.221916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T23:59:18.401490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
bangkok 2480
47.8%
phuket 2123
40.9%
ko 223
 
4.3%
phi 220
 
4.2%
pha-ngan 113
 
2.2%
koh 16
 
0.3%
samui 16
 
0.3%

Most occurring characters

ValueCountFrequency (%)
k 7083
21.7%
a 2722
 
8.4%
o 2719
 
8.3%
n 2706
 
8.3%
g 2593
 
8.0%
B 2480
 
7.6%
h 2472
 
7.6%
P 2456
 
7.5%
u 2139
 
6.6%
e 2123
 
6.5%
Other values (7) 3092
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26932
82.7%
Uppercase Letter 5191
 
15.9%
Space Separator 349
 
1.1%
Dash Punctuation 113
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
k 7083
26.3%
a 2722
 
10.1%
o 2719
 
10.1%
n 2706
 
10.0%
g 2593
 
9.6%
h 2472
 
9.2%
u 2139
 
7.9%
e 2123
 
7.9%
t 2123
 
7.9%
i 236
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
B 2480
47.8%
P 2456
47.3%
K 239
 
4.6%
S 16
 
0.3%
Space Separator
ValueCountFrequency (%)
349
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 113
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 32123
98.6%
Common 462
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
k 7083
22.0%
a 2722
 
8.5%
o 2719
 
8.5%
n 2706
 
8.4%
g 2593
 
8.1%
B 2480
 
7.7%
h 2472
 
7.7%
P 2456
 
7.6%
u 2139
 
6.7%
e 2123
 
6.6%
Other values (5) 2630
 
8.2%
Common
ValueCountFrequency (%)
349
75.5%
- 113
 
24.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32585
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
k 7083
21.7%
a 2722
 
8.4%
o 2719
 
8.3%
n 2706
 
8.3%
g 2593
 
8.0%
B 2480
 
7.6%
h 2472
 
7.6%
P 2456
 
7.5%
u 2139
 
6.6%
e 2123
 
6.5%
Other values (7) 3092
9.5%

Interactions

2023-06-15T23:59:01.519362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:39.718873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:42.487006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:45.461531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:48.282128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:50.671345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:53.073959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:55.038514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:57.055846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:59.198384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:59:01.672884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:40.032958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:42.742113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:45.747796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:48.540025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:50.893576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:53.220939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:55.216027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:57.261558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:59.399098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:59:01.824543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:40.270418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:42.979977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:46.014380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:48.811630image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:51.115076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:53.373412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:55.415339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:57.474853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:59.597367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:59:02.025707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:40.537672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:43.259121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:46.297966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:49.114134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:51.400663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:53.537185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:55.618410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:57.689434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:59.836622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:59:02.324392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:40.817985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:43.529041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:46.546724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:49.352990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:51.700362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:53.702329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:55.818351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:57.881459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:59:00.040544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:59:02.597311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:41.060486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:43.783504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:46.813556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:49.574767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:51.929943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:53.864384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:56.016195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:58.086560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:59:00.256499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:59:02.836010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:41.343467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:44.215313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:47.095859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:49.786555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:52.139710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:54.034992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:56.197694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:58.307985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:59:00.485923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:59:03.045751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:41.619666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:44.499560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:47.381010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:49.989954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:52.347679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:54.214792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:56.389361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:58.537586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:59:00.706356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:59:03.293352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:41.893659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:44.812451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:47.686279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:50.197122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:52.547405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:54.491134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:56.604389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:58.766697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:59:00.922386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:59:03.551153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:42.182036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:45.157335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:47.987684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:50.415210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:52.735439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:54.805255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:56.826752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:58:58.972309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T23:59:01.154842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-06-15T23:59:18.550453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
PriceStarsScoreReviewsBooked todayReal Guest Cleanlines ScoreReal Guest Facilities ScoreReal Guest Location ScoreReal Guest Service ScoreReal Guest Value for money ScoreSparkling cleanNewlyBuiltExcellentViewCheck In 24/7AirportTransferFront DeskValet ParkingFree WiFi In All RoomsSwimming PoolBarCoffeeDailyHousekeepingGolfKids clubOrigin
Price1.0000.3410.2670.0960.0260.2620.2960.2000.2380.2110.2180.0000.1160.0510.0980.0000.1660.0480.1810.1590.1910.0740.1200.2150.123
Stars0.3411.0000.0650.3930.2610.1110.159-0.0240.0980.0220.2410.0750.1580.3310.2840.3670.2550.1310.2890.3100.3120.4280.2140.3740.092
Score0.2670.0651.000-0.067-0.0340.8930.8750.6380.8390.8630.7460.0000.0930.1590.2180.2340.1030.1220.0960.1790.1690.2730.0890.1230.095
Reviews0.0960.393-0.0671.0000.568-0.060-0.0410.059-0.094-0.1060.0560.0000.1880.1050.0910.1160.0820.0000.0560.0830.0990.0650.0360.0820.048
Booked today0.0260.261-0.0340.5681.000-0.0110.0100.069-0.034-0.0520.0250.0310.1420.1430.0200.1550.0400.0280.0560.0450.0830.0980.0000.0800.100
Real Guest Cleanlines Score0.2620.1110.893-0.060-0.0111.0000.8940.5480.8590.8640.7620.0180.1040.1350.2320.1820.0940.1230.0810.1390.1710.2280.0800.1070.102
Real Guest Facilities Score0.2960.1590.875-0.0410.0100.8941.0000.5240.8260.8480.7310.0000.0840.1300.1830.1940.0950.1340.1270.1600.1770.2760.0800.1520.093
Real Guest Location Score0.200-0.0240.6380.0590.0690.5480.5241.0000.5290.5640.4180.0350.0660.1480.1570.1780.0900.1210.0570.1440.1600.2420.0710.0790.052
Real Guest Service Score0.2380.0980.839-0.094-0.0340.8590.8260.5291.0000.8070.6740.0350.0830.1200.2080.1770.0950.1190.0590.1410.1490.2280.0710.1080.109
Real Guest Value for money Score0.2110.0220.863-0.106-0.0520.8640.8480.5640.8071.0000.6410.0000.1040.1620.2090.2360.1050.1300.0780.1710.1680.3030.0800.1160.087
Sparkling clean0.2180.2410.7460.0560.0250.7620.7310.4180.6740.6411.0000.0220.1150.0720.1750.1380.0000.0000.0000.0430.0000.1160.0000.0000.099
NewlyBuilt0.0000.0750.0000.0000.0310.0180.0000.0350.0350.0000.0221.0000.0260.0400.0290.0000.0270.0280.0000.0000.0300.0240.0000.0170.000
ExcellentView0.1160.1580.0930.1880.1420.1040.0840.0660.0830.1040.1150.0261.0000.0250.0890.0630.0340.0000.0710.1460.1100.0820.0280.1150.254
Check In 24/70.0510.3310.1590.1050.1430.1350.1300.1480.1200.1620.0720.0400.0251.0000.1910.4450.1250.0760.0970.1910.1910.2450.1280.1660.150
AirportTransfer0.0980.2840.2180.0910.0200.2320.1830.1570.2080.2090.1750.0290.0890.1911.0000.2040.1810.0920.1390.2840.2620.2450.2070.1850.292
Front Desk0.0000.3670.2340.1160.1550.1820.1940.1780.1770.2360.1380.0000.0630.4450.2041.0000.1490.1080.0760.2590.2270.3540.1070.1660.177
Valet Parking0.1660.2550.1030.0820.0400.0940.0950.0900.0950.1050.0000.0270.0340.1250.1810.1491.0000.0060.0440.1850.1530.1360.1940.1710.089
Free WiFi In All Rooms0.0480.1310.1220.0000.0280.1230.1340.1210.1190.1300.0000.0280.0000.0760.0920.1080.0061.0000.0340.0770.0850.2560.0350.0000.141
Swimming Pool0.1810.2890.0960.0560.0560.0810.1270.0570.0590.0780.0000.0000.0710.0970.1390.0760.0440.0341.0000.1090.1080.0610.1570.0000.177
Bar0.1590.3100.1790.0830.0450.1390.1600.1440.1410.1710.0430.0000.1460.1910.2840.2590.1850.0770.1091.0000.4240.2620.1730.2240.186
Coffee0.1910.3120.1690.0990.0830.1710.1770.1600.1490.1680.0000.0300.1100.1910.2620.2270.1530.0850.1080.4241.0000.2320.1440.1700.085
DailyHousekeeping0.0740.4280.2730.0650.0980.2280.2760.2420.2280.3030.1160.0240.0820.2450.2450.3540.1360.2560.0610.2620.2321.0000.1310.1070.056
Golf0.1200.2140.0890.0360.0000.0800.0800.0710.0710.0800.0000.0000.0280.1280.2070.1070.1940.0350.1570.1730.1440.1311.0000.1640.160
Kids club0.2150.3740.1230.0820.0800.1070.1520.0790.1080.1160.0000.0170.1150.1660.1850.1660.1710.0000.0000.2240.1700.1070.1641.0000.164
Origin0.1230.0920.0950.0480.1000.1020.0930.0520.1090.0870.0990.0000.2540.1500.2920.1770.0890.1410.1770.1860.0850.0560.1600.1641.000

Missing values

2023-06-15T23:59:03.888688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-15T23:59:04.941789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

NamePriceStarsScoreSpokenLanguagesReviewsLocationSparkling cleanNewlyBuiltExcellentViewCheck In 24/7AirportTransferFront DeskValet ParkingFree WiFi In All RoomsSwimming PoolBarCoffeeDailyHousekeepingGolfKids clubBooked todayReal Guest Cleanlines ScoreReal Guest Facilities ScoreReal Guest Location ScoreReal Guest Service ScoreReal Guest Value for money ScoreOrigin
0Asia Hotel Bangkok (SHA Plus+)1194.07.9English, Thai27771296 Phayathai Road, Siam, Bangkok, Thailand, 1040000110101111100176.07.67.59.17.57.6Bangkok
1Rembrandt Hotel & Suites (SHA Plus+)1214.58.3English, Thai600119 Sukhumvit Soi 18, Klong Toei, Sukhumvit, Bangkok, Thailand, 101100011111101110032.08.58.18.48.38.6Bangkok
2Dream Hotel Bangkok (SHA Plus+)8064.58.4English, Thai1610910 Sukhumvit Soi 15, Sukhumvit, Bangkok, Thailand, 101100001111111111031.08.88.18.58.48.7Bangkok
3VIX Bangkok @ Victory Monument793.09.2English, Chinese [Mandarin], Thai143913-15 Thanon Ratchawithi, Chatuchak, Bangkok, Thailand, 104001000010100110024.09.39.09.59.49.3Bangkok
4The Berkeley Hotel Pratunam (SHA Plus+)2385.08.2English, Chinese [Mandarin], Thai61617559 Ratchathewi, Pratunam, Bangkok, Thailand, 1040000011101011100128.08.38.18.88.28.1Bangkok
5Ambassador Hotel Bangkok (SHA Plus+)1204.07.2English, Thai22016171 Sukhumvit Rd., Soi 11, Wattana, Sukhumvit, Bangkok, Thailand, 101100011010111110099.07.06.98.46.86.9Bangkok
6Grand President Bangkok (SHA Plus+)1334.07.1English, Chinese [Mandarin], Thai1202716 Sukhumvit Soi 11 , Sukhumvit, Bangkok, Thailand, 101100001111111110018.06.86.78.37.17.0Bangkok
7Solitaire Bangkok Sukhumvit 11694.58.2English, Thai923175/23 Soi Sukhumvit 13 Sukhumvit Road, Klongtoey-Nua, Wattana, Sukhumvit, Bangkok, Thailand, 101100011111111110025.08.68.18.18.58.2Bangkok
8Grand 5 Hotel & Plaza Sukhumvit (SHA Extra Plus)604.07.9English, Thai300487 Sukhumvit Road Soi 5, Klongtoey Nua, Wattana, Bangkok, Sukhumvit, Bangkok, Thailand, 101100001010101110025.08.17.38.67.97.6Bangkok
9Mövenpick Hotel Sukhumvit 15 Bangkok1075.08.3English, Thai3768Soi Sukhumvit 15, Sukhumvit, Bangkok, Thailand, 101100001111111110034.08.78.27.98.48.6Bangkok
NamePriceStarsScoreSpokenLanguagesReviewsLocationSparkling cleanNewlyBuiltExcellentViewCheck In 24/7AirportTransferFront DeskValet ParkingFree WiFi In All RoomsSwimming PoolBarCoffeeDailyHousekeepingGolfKids clubBooked todayReal Guest Cleanlines ScoreReal Guest Facilities ScoreReal Guest Location ScoreReal Guest Service ScoreReal Guest Value for money ScoreOrigin
4832Villa Paradiso1025.07.9English, Thai1Villa E, Malaiwana Estate,28/12 Moo 4, Tambon Sakoo, Amphur Thalang, Naithon, Phuket, Thailand, 83110000011010001100.05.07.57.57.510.0Phuket
4833Montana Hotel & Hostel Phuket703.09.6English, Thai1528/27 Patak Road, Karon, Phuket 83100, Karon, Phuket, Thailand, 83100100011010001000.010.07.510.010.010.0Phuket
4834Chomdao@Maikhao1794.07.5English9Rural Road Phuket 3033, Mai Khao, Phuket, Thailand, 83110000010010000000.06.06.02.06.02.0Phuket
4835Sealord Naithon Beachfront Villa940.08.9English, Thai3131/10 Naithon Beach Road, Naithon, Phuket, Thailand, 83110100001010111000.09.59.29.39.48.7Phuket
4836Living Room Guesthouse & Cafe Bar751.09.4English, Thai44516/6 Soi Centara, Muang, Phuket, Karon, Phuket, Thailand, 83100100000010111000.09.69.09.79.19.7Phuket
4837The Beach by Glitter House1833.09.5English, Thai40110/54 Kata Road, Kata, Phuket, Thailand, 83100100010011011000.09.49.69.49.59.4Phuket
4838Village House CAC123992.09.6English, French, Thai6158/12 Soi Bang Thao 7, Moo5, Bangtao, Surin, Phuket, Thailand, 83110100010010001000.09.79.79.010.09.7Phuket
4839The Beach by Glitter House533.09.5English, Thai40110/54 Kata Road, Kata, Phuket, Thailand, 83100100010011011000.09.49.69.49.59.4Phuket
4840Westkey Kamala villa8435.09.5English, Thai3Kamala, Phuket, Thailand100101001010000.09.39.39.310.09.3Phuket
4841Bcollection Resort1985.09.6English, Thai1Layan, Phuket, Thailand100000001010000.010.08.010.010.010.0Phuket